Addressing an idiosyncrasy in estimating survival curves using double sampling in the presence of self-selected right censoring
Addressing an idiosyncrasy in estimating survival curves using double sampling in the presence of self-selected right censoring
复制标题
DOI：
10.1111/j.0006-341x.2001.00333.x
复制
发表时间：
2001-06-01
期刊：
影响因子：
1.9
通讯作者：
Rubin, DB
中科院分区：
文献类型：
--
作者：
Frangakis, CE;Rubin, DB
我们从两个来源调查使用个体的跟踪样本来估计研究的生存曲线：(I)研究的提前终止，即行政审查，或(Ii)由于在行政审查之前丢失的数据而进行的审查，即所谓的退出。我们假设，对于整个队列的个人，行政审查时间独立于主体的固有特征，包括生存时间。为了解决因辍学而造成的审查损失，我们允许这可能是有选择性的，我们考虑进行密集的第二阶段研究，随后跟踪最初丢失的受试者的代表性样本并记录他们的数据。与调查方法中的双抽样设计一样，目的是提供关于辍学率的代表性子集的数据。尽管假设了后续样本的全部反应，但我们表明，在我们的设置中，行政审查时间并不独立于两个子组中的生存时间，即非辍学和抽样辍学。因此，分层Kaplan-Meier估计量不适用于队列生存曲线。此外，使用潜在结果的概念，而不是观察结果的概念，从而明确地将问题表述为数据缺失问题，可以揭示和解决这些复杂问题。我们提出了一种基于容易观察到的数据子集的可能性的估计方法，并对其在大样本下的性质进行了解析研究。我们通过模拟与实际髋关节置换研究中公布的存活率和脱落率相匹配的数据，在现实情况下评估了我们的方法。讨论了我们的设计和分析方法的局限性和扩展。
We investigate the use of follow-up samples of individuals to estimate survival curves from studies that are subject to right censoring from two sources: (i) early termination of the study, namely, administrative censoring, or (ii) censoring due to lost data prior to administrative censoring, so-called dropout. We assume that, for the full cohort of individuals, administrative censoring times are independent of the subjects' inherent characteristics, including survival time. To address the loss to censoring due to dropout, which we allow to be possibly selective, we consider an intensive second phase of the study where a representative sample of the originally lost subjects is subsequently followed and their data recorded. As with double-sampling designs in survey methodology, the objective is to provide data on a representative subset of the dropouts. Despite assumed full response from the follow-up sample, we show that, in general in our setting, administrative censoring times are not independent of survival times within the two subgroups, nondropouts and sampled dropouts. As a result, the stratified Kaplan-Meier estimator is not appropriate for the cohort survival curve. Moreover, using the concept of potential outcomes, as opposed to observed outcomes, and thereby explicitly formulating the problem as a missing data problem, reveals and addresses these complications. We present an estimation method based on the likelihood of an easily observed subset of the data and study its properties analytically for large samples. We evaluate our method in a realistic situation by simulating data that match published margins on survival and dropout from an actual hip replacement study. Limitations and extensions of our design and analytic method are discussed.